When you watch a conductor at work, they guide the flow of music with their baton. If a musician makes a mistake, they give a subtle tap to get them back on track. What they never do is jump down, grab the instrument, and start playing themselves.

Yet that’s exactly what many leaders do. They see a problem, dive in, and solve it themselves rather than asking: “What environment do I need to create so this person can perform better?”

In the latest episode of the Plan10x podcast, host Manoj Kohli sits down with Phil Gadzinski—co-author of the Amazon bestseller Govern Agility and former APAC Head of Transformation at Bupa—to explore how leaders must rethink their approach to governance, team development, and their own roles.

Here are the key leadership lessons from their conversation.

1. Evolve Beyond Industrial-Age Leadership

Most management training is still rooted in Taylorism—an early-1900s approach of measuring how many pins a worker can make in a day and rewarding higher output. Business schools bolt on contemporary ideas, but underneath it all sits this outdated foundation.

This matters because statistical management models work for factory floors, not creative knowledge work. Great technical talent gets promoted to management, but when nobody teaches them that leadership is a completely different job, they keep doing what they know—the technical work—instead of leading.

What needs to change: Recognize that leadership requires different skill sets—however, command-and-control isn’t one of them. Adopt training programs that align with your culture, and invest heavily in developing your talent.

2. Role Modeling Matters More Than Mandates

Phil shared a thoughtful example from ANZ Bank. A CEO spent 10 years shifting the culture—breaking silos, building cross-functional teams, changing how people think and work. Financial results improved steadily over the decade.

Within three months of that CEO leaving, it all unraveled. A new CEO introduced new ideas, and the organization reverted almost immediately to where it had started. Why? The role modeling disappeared.

As Phil observes: “I don’t think you can force people to think differently.” You can’t mandate a new mindset through training programs or executive decrees. But you can find small, visible, repeatable behaviors that demonstrate what you want the culture to become.

Another organization Phil knows decided that leaders would wash their own coffee cups. It sounds trivial, but walk into any office kitchen full of dirty cups, and you immediately question whether people take responsibility. Clean kitchens signal that people take pride in their workplace.

How to make change stickier: Tangible actions you can demonstrate consistently are more effective than mandating mindset shifts. (Read: Your behavior matters more than your words.)

Read Next: Why Proactive Change Management is the Right Approach to Transformation (Part 1)

3. Embrace Trust But Verify

Traditional governance systems assume low trust. Someone far from the work needs assurance that things are being done correctly, so layers of oversight, approvals, and checkpoints get added.

Modern, agile systems require the opposite: high trust with appropriate verification. This means creating transparency where it makes sense without drowning everyone in information. Radical transparency sounds good in theory, but it leads to overload and paralysis. Sensible transparency means finding the right zone for your context.

A new approach to trust: Move from low-trust to high-trust governance. Identify areas where you don’t need all the answers and which tools and teams can quickly get them, when needed.

4. Develop Curiosity and Empathy

Phil referenced the TV show Ted Lasso and its memorable line about being curious rather than judgmental. For leaders facing relentless change—new AI tools, shifting market conditions, evolving customer expectations—curiosity is survival equipment.

A learning culture adapts to change better than a fixed culture. And a learning culture starts with leaders who demonstrate genuine curiosity and show empathy for what their teams are experiencing. Your people are under pressure, too. They’re being asked to learn new tools, adopt new processes, and deliver results simultaneously.

A learning mindset means: We can’t say two things enough: First, think about it from the perspective of your people—they read job outlook reports as much as you do. Saying “just use AI” rings like a clanging cymbal. Second, don’t passively delegate AI learning to your teams. Leaders must personally build new skills and engage with AI tools to be relevant and effective.

5. Balance Workforce Development

When comparing your team’s capabilities with where you need to go, start by assessing the scope of change. Is the new work 30% different from what they do now? Or 70% different? This determines your investment strategy.

If it’s a modest shift, training programs can bridge the gap. If it’s a significant departure, you’ll need to balance upskilling existing employees while bringing in new expertise. The goal is to keep current team members engaged and learning while adding complementary skills.

Managing capability transitions: Help current employees transition from creator to composer—support them as they evolve from manual creation to orchestrating AI and automation.

Read Next: Beyond Utilization Rates: How AI is Solving the Talent “Visibility Gap”

6. Be Human

Leaders at all levels of an organization face immense pressure to set strategy, meet performance targets, deliver business outcomes, learn new tools, and change how they work. It’s relentless—but you don’t have to have it all figured out.

Give yourself grace: Accept that you’re human. Take a breath and step back. Focus on progress, direction, and continuous improvement rather than having all the answers.

The Bottom Line

Modern leadership is about creating environments where others can succeed, modeling the behaviors you want to see, staying curious, and accepting that you’re moving through uncertainty alongside your team—not from a position of having all the answers.

What Else You’ll Learn

While this blog post focuses specifically on leadership, the full podcast conversation between Manoj Kohli and Phil Gadzinski covers much more ground on modernizing governance for the digital age. Listen to the complete episode to discover:

• Why 90% of AI experiments are failing
• Real-world governance case studies
• How to become truly data-driven in your delivery systems
• The drain of organizational structure on delivery speed
• When to choose radical change versus continuous improvement

Today’s report on the next-generation evolution of AI is one you don’t want to miss. Read on as Planview’s Chief Data Scientist, Dr. Rich Sonnenblick, explores why most AI pilots never scale, and how connected intelligence moves organizations from experiments to enterprise impact.

These days, talking with business and technology leaders about AI often centers on a critical topic: AI maturity.

Most organizations have experimented with AI and are educating their workforce. Many others have built pilots and capabilities, using gen AI – including LLMs – to increase individual productivity and efficiency. In fact, McKinsey’s latest Global Survey on AI reports that 88% of companies use gen AI in at least one business function (up from 78% a year earlier).

As adoption rates climb, so does the desire of leaders everywhere for a resounding ROI on AI. The deciding factor? According to MIT, it’s AI maturity.

MIT’s AI maturity framework outlines a four-stage journey:

• In stage 1, organizations experiment and prepare for AI;
• In stage 2, they build AI pilots and capabilities;
• In stage 3, they develop AI ways of working;
• In stage 4, they become AI-future ready.

Their research reports that “organizations see the greatest financial impact in moving from Stage 2 to Stage 3 of AI maturity,” underscoring the benefits of embedding AI use across the business.

And yet, according to McKinsey, vertical or function-specific use cases – where the greatest potential for financial return lies – seldom make it out of the pilot phase because of technical, organizational, data, and cultural barriers.

We hear a similar sentiment about the factors that affect AI maturity.

Read Next: AI Trend Report: 4 Paradigm Shifts to Prepare For

What’s Really Delaying AI Maturity?

Some companies tell us they’re strapped with legacy system integration constraints or siloed departments. Others disclose that poor data quality – or the age-old issue of resistance to change – is preventing them from scaling AI at their desired pace.

To us, these barriers spring from a common source: The inherent complexity of digital connected work.

Digital connected work is the interdependent web that weaves together every critical element of an enterprise, from idea to outcome. People, technology, data, dependencies, and other critical elements of a business are all linked together. In this environment, one of the biggest risks is disconnection – silos that decrease alignment and increase delays, along with other negative impacts.

We see silos impeding AI maturity in several ways. One example is when an AI tool can’t access capacity constraints from resource management systems or dependency data from delivery teams. As a result, the AI can’t identify which strategic initiatives will be at risk, and delivery teams can’t see how their work connects to top-level Objectives and Key Results (OKRs).

Without connected context, each department stays stuck running isolated experiments that rarely scale to drive enterprise-wide value.

Advancing AI maturity requires bridging these silos with intelligence that understands the full context of your business, not just individual functions. This is where AI for digital connected work comes in – and here’s where we can help.

The Anvi Advantage for Digital Connected Work

Planview Anvi is the next-generation enterprise AI for digital connected work, combining the full spectrum of AI capabilities across our end-to-end platform – from our Connected Work Graph to generative and agentic AI.

Unlike general-purpose AI tools, Anvi understands the relationships between portfolio investments, resource capacity, strategic objectives, and cross-team dependencies. Anvi operates on Planview’s comprehensive data fabric, leveraging deep domain expertise to provide guidance and intelligent actions that help you prioritize strategic investments and deliver positive outcomes.

Cognizant enhanced their delivery productivity, accelerated user adoption, and improved project risk identification and mitigation with Anvi.

After establishing their PPM foundation with Planview, Cognizant moved from piloting gen AI to embedding AI capabilities directly into workflows across governance, training, and technical reporting domains.

As Estela Lauricella-Thota, Senior Director of Technology Transformation, explains: “We’ve built a solid foundation of data and processes. This positions us very well to leverage gen AI and Planview Anvi to drive the next wave of transformation – enhancing productivity, accelerating adoption, and achieving higher levels of PPM maturity.”

Here’s the breakdown of Anvi’s capabilities.

Data Fabric

This is the foundation that enables AI to scale beyond isolated pilots. Our multi-year investment in developing a connected work semantic layer means Anvi goes beyond aggregating data, because it understands what that data means in your business context.

It knows how a strategic objective connects to funded initiatives, which connect to team capacity, and which connect to individual work items and dependencies across tools. 

Anvi operates across your entire work ecosystem, identifying risks and opportunities that span multiple systems. These are patterns impossible to see when data lives in silos.

Read Next: Why Data Architecture is the Hidden Key to Agentic AI

Connected Work Graph

This is where Anvi moves from productivity tool to strategic execution partner. Connected Work Graph creates a living map of how work connects across teams, projects, and outcomes.

What used to require analyzing rows of spreadsheets now happens visually.

Users can trace impact chains from execution activities to business objectives, identify hidden cross-portfolio dependencies before they derail outcomes, and eliminate structural bottlenecks that delay strategic initiatives.

Anvi continuously learns from your organizational work patterns, surfacing optimization recommendations that reduce coordination overhead. This enterprise-scale intelligence connects every dependency across your organization in real time, enabling AI to scale beyond narrow use cases into coordinated, cross-functional capabilities.

Conversational AI

Ask Anvi questions in natural language – such as “Which initiatives are at risk of missing Q3 deadlines?” or “Where have we overextended engineering resources?” – and get data-backed answers that pull from across your connected systems.

Beyond answering questions, Anvi provides contextual recommendations based on your role and embedded domain expertise, guiding you on portfolio prioritization, resource allocation, and strategic decisions.

You can also take intelligent actions directly through conversation: update projects, generate executive summaries, and create status reports. Instead of switching between tools to gather data, analyze it, and then act on it, you have a conversation that does all three.

Custom Agents

Build intelligent workflows that go farther than individual productivity. Start with prebuilt agents for common scenarios, such as portfolio health checks or resource planning, then customize them with your organization’s specific processes and guidelines. Schedule these agents to run automatically, delivering regular executive briefings or scanning for data quality lapses in upcoming work while you focus on more strategic tasks.

In the early days of Anvi – when the idea of agentic AI was still new – Planview staff used Anvi to create 200+ custom agents during a 60-minute workshop.

Read Next: Mapping the Future of AI Agents in the Enterprise

In-App

Risk detection proactively flags potential roadblocks and timeline risks based on patterns in your work data. Sentiment analysis automatically tracks team morale and stakeholder engagement across communications. Performance anomaly detection identifies unusual patterns in delivery metrics that need attention.

Plus, when you open Planview.Me, personalized insights surface the most relevant priorities for your specific role and workflow. The intelligence comes to you, embedded in the flow of work you’re already doing.

Moving Forward in the Age of AI

Today, it’s not enough to have high-quality data feeding your AI initiatives; that data must also be seen by your AI as the complex, connected web of resources, work, and objectives that it is. Advancing AI maturity requires deploying AI that understands your entire enterprise as a connected system.

That’s what Anvi delivers. It’s an innovation we’re proud to offer as we continue partnering with our customers to achieve the outcomes that matter most.

See Anvi in action with the Anvi on-demand demo or visit planview.com/ai.

“Who’s available to take on this project next month?”

We’ve digitized the tasks, but we haven’t solved the chaos. And here’s the kicker: research shows that only 27% of work actually aligns to strategic objectives. The problem isn’t a lack of tools. It’s a lack of connection.

The Root of the Resource Management Blindspot

According to recent PM Solutions research, the primary failure point in resource management today isn’t identifying resources—it’s integrating the data. Organizations are drowning in disconnected information, where critical capacity data lives in silos across multiple systems.

But data silos are just the technical problem. There is also a second issue that stems more from work environments: different teams work in fundamentally different ways. Some are agile, others follow waterfall methodologies, and many fall somewhere in between with hybrid approaches. This fragmentation, which is rooted in personnel and culture, creates an additional translation problem that goes beyond simple data integration.

How do you compare capacity across teams that measure work in completely different ways?

Without the ability to both integrate scattered data and translate across different ways of working, leaders are left guessing. The 2025 State of Resource Management Report confirms this, identifying capacity planning as the single biggest challenge facing leaders this year.

The Strategic Cost

When visibility fails, the consequences ripple far beyond missed deadlines. Gallup’s State of the Global Workplace report reveals that disengagement contributed to a staggering $438 billion loss in productivity in 2024, with manager engagement dropping to just 27%. But burnout is only a symptom. Bad resource allocation is the core problem.

This connects directly to strategy execution. Only 20% of executives feel confident that resources are properly allocated to execute strategy—which directly contributes to why only 27% of work aligns to strategic objectives in the first place. It’s a vicious cycle: poor visibility leads to poor allocation, which leads to strategic misalignment, which undermines confidence in the entire planning process.

This is no longer just an operational headache. It’s a board-level issue that threatens both strategic opportunities and organizational health.

How AI Changes the Game

Contextual Understanding Across Systems

AI-powered solutions like Planview Anvi connect disparate tools through an integrated data fabric with 60+ connectors, creating what resource managers have long dreamed of: a single pane of glass. AI does the heavy lifting of normalization, so managers don’t have to toggle between ten different dashboards to understand team capacity.

Organizations reap even more benefits when this is done in a way that doesn’t add another tool to an already crowded stack. AI that can embed within existing workflows creates a unified view without adding disconnection. More importantly, it surfaces hidden risks and dependencies that would otherwise remain invisible until they cause problems.

Predictive Capacity Planning

McKinsey’s State of AI research highlights a critical shift: high-performing organizations aren’t using AI just to automate tasks—they’re redesigning workflows entirely. In resource management, this means AI can forecast bottlenecks before they happen, shifting the entire discipline from reactive firefighting to predictive strategy.

Instead of discovering you’re three people short halfway through a project, AI identifies the constraint during planning, giving leaders time to make informed decisions about priorities, timelines, or resource acquisition.

Skill-Based Resource Optimization

A key challenge that resonates across industries is organizations struggling to identify the skills they already have. According to research on the AI skills gap, companies are sitting on hidden talent but lack the visibility to leverage it effectively.

AI solves this by moving beyond rigid job titles to capability-based matching. It can scan project histories, certifications, and work patterns to find the best person for a job—even if they sit in a different department or haven’t done that exact work before. It matches capabilities to needs, uncovering talent that traditional resource databases would miss.

From Administration to Strategy

The future of resource management isn’t about better utilization reports or more sophisticated Gantt charts. It’s about treating resource management as a core part of strategy execution, not an afterthought.

AI enables realistic commitments and strategic agility by giving leaders something they’ve rarely had: data confidence. With accurate, predictive visibility into capacity, leaders can say “no” or “not yet” to new work, protecting the integrity of the strategy they’ve already committed to. That’s not just efficient resource management—it’s strategic discipline.

The organizations that thrive in the next decade won’t be those that squeeze every percentage point out of utilization rates. They’ll be the ones that use AI to connect strategy to delivery, ensuring the right people work on the right things at the right time.

Delivery, at its core, behaves less like a predictable assembly line and more like weather: sometimes clear and calm with steady throughput, other times volatile, unpredictable, and capable of derailing even the most confident commitments.

The Forecast Matters as Much as the Data

Most engineering leaders live in this atmosphere daily. Turbulence arises in the form of unplanned work, storms build quietly in an aging backlog, and crosswinds form in dependencies no one anticipated. Tools surface data, dashboards capture the trail behind us, teams speak to progress as they see it in the moment, but even end-to-end visibility does not replace foresight.

Leaders don’t simply need to know what is happening; they need to understand why it is happening: where the wind is shifting, how patterns emerging beneath the surface could shape the weeks ahead.

This is where Planview Viz and Planview Anvi come in. They’re designed to spot and avoid risk before it ever causes turbulence, much like a weather forecast. Together, they give leaders the relevant information required to navigate complexity with clarity instead of instinct.

Engineering teams already have reams of data. But data alone tells you how much rain has fallen, not whether a storm is building. Burn charts show progress, not the overloaded team completing the work. Tooling highlights what is blocked, not what will be blocked unless leaders adjust team structures or clarify priorities.

Leaders need forecasting so they can steer around the storm.                                                

Viz becomes the radar scanning delivery airspace, identifying storm pressure long before a deadline slips. Meanwhile, Anvi becomes the meteorologist, interpreting conditions, synthesizing probability, and translating raw signals into guidance that leaders can act on with confidence.

Early Warning Systems for Delivery Risk

Weather rarely becomes dangerous all at once. It builds quietly and is marked by rising pressure and subtle wind shifts.

Delivery risk behaves the same way.

A missed deadline is never the first indicator of a problem; there are early indicators, stalled initiatives, aging work, and slow handoffs.

Leaders don’t need awareness once the storm begins. They need a solution that notices and flags the first drop in temperature.

The combination of Planview Viz and Anvi transforms engineering operation capabilities. Instead of responding to risk that becomes apparent through escalations, they scan delivery flow continuously, identifying the kinds of signals humans overlook – not because leaders lack skill, but because it’s impossible to monitor every weak signal across every team in real time.

Learn more: Watch this on-demand demo to see how Viz and Anvi drive faster, smarter decision making.

Organizations that use an Anvi agent to generate a Monday morning summary get more than a status readout. They start their weeks already knowing:

• Where work is aging faster than it is moving
• Which teams are overloaded
• Which initiatives are drifting, and if gaps are widening between plan and execution
• Where new unplanned work is eating up team capacity

No guesswork. No stitching insights together from multiple Jira instances, or from a complicated network of interconnected systems. Instead of reacting in firefighting mode, leaders enter standups and PI reviews with context already formed and ideas for action.

And this is just the beginning of what early warning looks like.

Anvi doesn’t simply list symptoms – it interprets them and provides actionable insights. It can break down aged work by category, exposing whether the storm is coming from technical debt, external blockers, or work item complexity. It can correlate stalled stories with ownership patterns, revealing where a bottleneck is being created because a single individual is wildly overloaded. It can even detect abandoned work, quietly shelved without official cancellation.

Where traditional reporting tools hold up a window, Viz and Anvi function like radar, sensing movement beyond visibility and surfacing actionable intelligence without manual analysis.

A leader equipped with early warning doesn’t ask, “What happened?” Instead, they ask, “Where should we intervene first?”

And that shift – from interpretive to informed decision – is the moment delivery resilience begins to scale.

Attribution Analysis Reveals Climate, Not Just Weather

Any pilot can look out the window and see rain. What they can’t see is whether the storm is passing or strengthening, or when the next one is forming beyond the horizon. In engineering, charts and metrics operate in the same way. They show when velocity is down, WIP is up, and throughput is inconsistent.

These are symptoms, not root causes or patterns. They just tell you it’s raining, not what caused the clouds to form.

Attribution analysis is the difference between observing weather and understanding climate. Viz detects shifts that indicate systemic change or issues in delivery behavior: surges in unplanned work, unusual turbulence in flow distribution, or recurring bottlenecks at specific points in the process. Then Anvi takes that raw movement and explains the why, the how, the where, and often the what next.

Delivery might surge dramatically during one period, fall sharply the next, then begin a slow recovery. A traditional dashboard could plot the curve, perhaps even highlight regressions or volatility. But it takes analysis of thousands of data points to understand the story beneath the pattern:

• What drove the spike?
• Was it staffing capacity? Shifts in work balance? Fewer dependencies?
• Why did flow regress afterward?
• Did teams absorb unplanned work? Did priorities pivot?
• What changed in the system to enable recovery?
• Did new practices take hold? Did WIP limits improve? Did risk shrink?

Viz surfaces the signals while Anvi translates them into causality. Instead of dumping metrics alone onto a leader’s desk, Anvi connects the numbers to the pressure systems moving beneath them:

“Delivery volatility increased following a shift to higher-complexity work. Flow stabilized after teams reduced WIP and clarified ownership boundaries. Recommendation: reinforce these patterns and monitor dependency wait times, where pressure is rising again.”

Insights like these teach leaders which patterns to reinforce, which to avoid, and where the system may destabilize if left unattended. And instead of consuming hours of analyst time, the insight arrives in seconds.

Even more importantly, attribution prevents a knee-jerk reaction. Without it, a drop in throughput looks like performance decline. With attribution, leaders may discover the drop resulted from intentional investment in defect resolution – a calculated decision to address technical debt and strengthen future delivery stability.

Attribution turns turbulence into context. It prevents overcorrection, enables smarter prioritization, and helps organizations distinguish between healthy slowdown and true delivery risk.

And when this capability is combined with predictive modelling, attribution becomes an accelerator. Leaders no longer need to operate on instinct alone. Instead, they operate with atmospheric intelligence.

Predictive Delivery: When Forecast Becomes Flight Navigation

When engineering leadership crosses from observation into prediction, something fundamental changes: decision-making shifts from reactive course correction to intentional route planning.

Today, Viz gives leaders the ability to see inside delivery systems: where work is flowing smoothly, where turbulence is forming, where clogged queues resemble slow-moving storm fronts. Anvi interprets those signals, translating flow patterns into insight. But the next evolution – predictive delivery modelling – not only describes weather conditions but also simulates how they will evolve.

Instead of asking, “What is happening right now?”, leaders can ask:

• When will this initiative be completed if we maintain current velocity?
• Where will we encounter turbulence if unplanned work increases?
• What capacity changes are needed to deliver our Q3 commitments?

Traditional planning is like flying a plane while writing your own maps as you go. Predictive analytics changes that.

Monte Carlo simulations, state-transition modeling, and probability distributions forecast outcomes based on real historical behavior, not gut feel or best-case optimism. Anvi becomes the co-pilot, reading weather patterns and suggesting flight paths with clarity and confidence, reducing guesswork to near zero.

Suddenly:

• Delivery dates become probabilities instead of estimates
• Capacity planning becomes scenario-based, not hope-based
• Portfolio reviews shift from retrospective debate to future-oriented action
• Risk is anticipated early and doesn’t derail committed deadlines

A leader could ask Anvi:

“Show me the projected completion range for all in-flight initiatives, highlight anything below 70% confidence, and suggest interventions.”

Anvi could respond with:

• Features A, B, and C are on track with high confidence.
• Feature D is at risk due to dependency drag and WIP inflation.
• Feature E has a 40% probability of delay under current load
• Recommended mitigation: reassign two contributors or reduce scope by 15%

No spreadsheet wrangling. No manual timeline smoothing. No last-minute executive escalations.

Engineering moves from weather-reactive to weather-aware.

Because when leaders can see not only the rain but the wind currents guiding it, they stop asking how to respond and start asking how to steer.

Predictive visibility gives organizations more options, more resilience, and more control – qualities that separate teams who land reliably from teams who are always circling the runway.

Forecasting becomes flight navigation. Confidence becomes capability. Delivery becomes intentional, not incidental. And the turbulence that once felt inevitable becomes avoidable.

Why This Matters for Engineering Leadership

Engineering isn’t just a function on an org chart – it’s the engine of value creation. But even the strongest engine fails when signals conflict, or leaders are left steering through uncertainty without the ability to anticipate change. The difference between teams that deliver predictably and those that operate in constant recovery mode rarely comes down to talent or effort. It comes down to foresight.

With Viz and Anvi, engineering leadership no longer relies on lagging indicators, unwritten institutional knowledge, spreadsheet archaeology, or retrospective interpretation. Leaders can finally sense pressure early, understand the atmosphere shaping delivery behaviors, and navigate complexity with deliberate choices rather than reactive escalation.

This is why the shift matters:

• Leaders spend less time gathering data and more time making decisions
• Work stops quietly stalling and starts elevating when risk emerges
• Prioritization becomes data-based instead of politically negotiated
• Flow becomes measurable and predictable
• Teams stop firefighting and start executing with confidence

When engineering operates with foresight instead of hindsight, organizations gain a new level of strategic control. They respond faster because they can spot and prevent turbulence before it forms. They guide progress instead of reporting on it.

The companies winning today aren’t the ones with perfect plans. They’re the ones with visibility into the weather around their plans. With radar to read the atmosphere (Viz) and a co-pilot to interpret it (Anvi), engineering leaders unlock something far more valuable than efficiency. They unlock certainty. Certainty around delivery, risk, and strategic commitments. And certainty, in a world of accelerating complexity, is a competitive advantage.

See predictive delivery in action: Watch the on-demand demo of Planview Viz and experience real-time forecasting, flow intelligence, and AI-assisted delivery insight.

While there’s no single definition of the term, being AI-first entails integrating the ever-advancing technology into a company’s core – as their new North Star. It means that AI becomes the prevailing mindset, permeates the company culture, and even points the compass for decisions about operations, workflows, and more.

It’s a vital evolution, and companies want to jump in headfirst. But according to McKinsey Partner Megha Sinha, who counsels Fortune 500 C-suites on AI and product transformation, companies can’t be AI-first without being product-first.

“[A] product operating model is the absolute critical foundation to leapfrog into an AI-first organization. You cannot bypass that and go there,” Sinha declared in a recent webinar with Planview, referencing the importance of a product operating model for achieving bottom-line benefits.

So what exactly is happening in this phase of the AI era? And why does your operating model matter so much? In this blog post, we’ll explore the four paradigm shifts AI is bringing to enterprises and what organizations that want to accomplish more with AI must do to prepare.

Listen to the full webinar, “Product Operating Model Principles Transforming Fortune 500s,” for board-ready examples for product operating model investments, implementation roadmaps, and success metrics, all with experience-based commentary from Sinha.

Read Next: The Case for Adopting a Product Operating Model

The Four Paradigm Shifts to Prepare For

Based on early proof-of-concepts and scaled implementations with clients, Sinha outlined four major shifts that AI will bring to enterprise organizations. Some leading software companies in Silicon Valley are already experiencing these changes. For Fortune 500 companies across industries that aren’t yet immersed in AI, these changes are rapidly approaching.

1. Product Development Lifecycles Will Transform Completely

The traditional product development lifecycle – where someone writes a product requirements document (PRD), translates it into epics and user stories, then begins building – is becoming obsolete.

“Your product development lifecycle will transform significantly in the world of AI,” Sinha explained. “There will be a complete redesign of the workflow.”

What’s replacing it?

• Spec-driven development and testing. Instead of lengthy PRDs, your prototype becomes your new PRD. The cycle from concept to working code compresses dramatically.
• Compression across all phases. Organizations will see compression in product discovery and viability phases, in build and test phases, and in monitor and operate phases. What takes weeks now will take days. What takes days will take hours.
• A shift in bottlenecks. “In the current world, organizations say that my software developers can’t write code fast enough,” Sinha noted. “Tomorrow, your bottlenecks will shift from code writing to code review.”

In other words, the constraint won’t be getting code written. AI agents will handle that. The constraint will be ensuring the right things are being built and maintaining quality standards.

This shift has massive implications for how organizations staff teams, what skills they need, and where senior talent focuses their time.

Read Next: Why Your AI Tools Aren’t Working Yet

2. New Ways of Working Will Emerge

Sprint cycles are about to get much, much shorter. “Sprint cycles could look as short as a day,” Sinha said.

Here’s how a one-day sprint could work: AI agents and humans working together complete an entire cycle in 24 hours – quick customer research, rapid prototype development, immediate customer feedback, AI-generated code, automated testing. Done.

“All of that is happening…in the span of a day or less, whereas it used to take two weeks to do that,” Sinha said.

But, Sinha advises, you must have the right operating model foundation to accelerate sprint cycles effectively. You need:

• Clear product ownership and decision rights
• Empowered teams that can move quickly
• Outcome-based goals rather than output-focused metrics
• Continuous deployment capabilities
• Tight feedback loops with customers

Without these elements in place, faster development cycles will create chaos rather than value.

Watch Now: Five Steps to a Product Operating Model with Dr. Mik Kersten

3. Team Structures Will Evolve Dramatically

Sinha made a striking prediction: “Nearly 100% of your humans will have new roles in two years.”

Why? Because AI agents will handle full development cycles that humans manage now. This doesn’t mean eliminating people – it means evolving what people do.

“Your developers will now start to focus more into your specialized R&D roles, will start to focus more on higher-order problems, and will transform into what we call definers, builders, and a small number of additional roles.”

The shift is from executor to definer. From writing every line of code to defining what should be built and why. From tactical implementation to strategic direction.

And team sizes will shrink. Sinha referenced Amazon’s famous “two-pizza team” concept—teams small enough to be fed with two pizzas. In an AI-enabled environment, that becomes a “one-pizza team.”

Smaller teams, more focused, solving higher-value problems while AI handles more of the execution.

But this raises critical questions for every organization:

• How do you reskill your current workforce for these new roles?
• What does career development look like when traditional engineering paths change?
• How do you attract and retain talent in this new model?
• What happens to people whose roles are most affected by AI automation?

Organizations that address these questions proactively will have a significant advantage over those that treat them as non-essential actions.

Read Next: Mapping the Future of AI Agents in the Enterprise

4. Velocity Will Accelerate, But Focus Must Shift

The combined effect of these three shifts? Dramatic acceleration in what teams can accomplish.

Sinha outlined the potential impact:

2-3X faster feature development. The time from idea to deployed feature will compress significantly. Organizations already moving quickly will move even faster.

5-10X improved innovation capacity. This is the big one. When teams spend less time on execution mechanics, they can explore more ideas, test more hypotheses, and innovate at a pace that’s hard to imagine now.

More time on value-added work. Instead of spending time on translation between teams, coordination overhead, and manual processes, people focus on customer problems and creative solutions.

But the key thing to remember here is this: Velocity without direction is chaos.

As individual productivity accelerates through AI, the strategic functions become even more important:

• Defining the right problems to solve. What aligns with strategy? What delivers customer value? What creates competitive advantage?
• Coordinating across teams. When teams can build faster, dependencies and integration points become more critical, not less.
• Connecting work to strategy. With more initiatives possible, ensuring alignment to business objectives is paramount.
• Managing flow and removing bottlenecks. As Sinha noted, bottlenecks shift. Organizations need to identify and address them quickly.

In the planning and portfolio management space, this means connected work capabilities like Objectives and Key Results (OKRs) – linking individual initiatives back to strategic objectives – become essential infrastructure, not nice-to-have features.

Read Next: Paving the Way: AI Is the Advantage Strategy Leaders Use

What Does This All Mean?

These four paradigm shifts are already happening in leading organizations. Is your company ready to capitalize on these changes? Remember, readiness isn’t just about AI tools or technology investment. It’s about having the operating model foundation that allows you to absorb and productively use these capabilities.

Discover the five pillars of the product operating model and get detailed assessments to uncover exactly where your organization stands today: Download your copy of “Creating an Outcome-Based Enterprise.”

About the Expert: Megha Sinha is a Partner at McKinsey & Company based in New York. She counsels C-suite executives on product, technology, and AI topics and has led over 25 large-scale product operating model transformations across financial services, fintech, retail, telecommunications, and healthcare in the past four years.

We’re building agents, piloting or scaling AI-powered tools across various disciplines, and continuously improving our individual skills. The momentum we’ve built this year is ready to launch us into 2026, more AI-savvy than ever.

But before we do, let’s take a look at a few of the year’s biggest highlights.

Back to School Month

September was AI Month here at Planview. We made a cross-company commitment to explore AI together through some “back-to-school” learning, experimentation, and collaboration.

Across four weeks of collaborative education and friendly competition, teams from every corner of Planview found new ways to use AI to simplify work, spark creativity, and strengthen connections.

Exploring AI Our Own Way

Everyone at Planview was invited to chart their own course – to reserve time for exploring, experimenting, and building confidence with AI. That ranged from curated options for self-guided learning to expert seminars and allowed us all to see how AI can make work more meaningful, and less manual.

The spirit of exploration came to life in our “Back-to-School” Best AI Agent competition, where employees built their own agents in Planview Anvi^TM, the AI for connected work we launched in October.

In one 60-minute workshop, the participants – most of whom had never built an agent before – created 200+ agents with Anvi.

The agents were built to tackle real, everyday challenges, proof that innovation doesn’t always start with a big idea, but with a small, meaningful improvement.

The winning agents were recognized for saving hours of repetitive work, keeping information consistent and up to date across multiple AgilePlace boards, and helping users organize and prioritize work more effectively.

Meet Anvi: Your AI for Connected Work

Hackathon Week 2025

Perhaps the beating heart of our AI mission at Planview was our global Hackathon, a chance for teams across the globe to dream big and put our mission – to build the digital future of connected work – into practice.

Hundreds of participants came together to create solutions that connected people, products, and possibilities. Nearly 88% of projects explored AI in new and creative ways – a powerful signal of where we’re headed next.

Each region crowned its own winner, but the shared theme was clear: when we build together, we build better.

Our Values in Action

Our AI journey isn’t just about technology – it’s about people. Every idea, agent, and prototype reflected is what makes Planview, well, Planview:

• We take our mission (but not ourselves) seriously, taking time to approach AI with curiosity, humility, and humor.
• We build together, demonstrating once again how we are capable of collaborating across teams, time zones, and disciplines.
• We value our differences, a month of drawing on unique perspectives to solve shared challenges.
• We strengthen connections, across all things, between products, people, and ideas.
• We do the right thing, learning how to use AI thoughtfully and responsibly.

The Momentum Rolls On

While 2025 ends soon, the momentum around AI at Planview won’t stop. The work we put in all year – from our joint learning sessions and competitions to our global hackathon – is already inspiring new initiatives across Planview. And there’s much, much to come.

Speed and flexibility feel less like differentiators and more like survival skills.

If complex approvals, disconnected tools, and reactive reporting are still slowing you down, you’re not just behind schedule anymore. You’re at risk of losing your competitive foothold entirely.

Only 28% of organizations say they can adapt to change quickly, a sharp drop from 40% in 2021

The State of Strategy Execution: Balancing Speed, Adaptation, and Control

So, how do you overcome this obstacle?

It starts with rethinking your portfolio management approach. Spreadsheets and manual processes can’t take you to the front of the pack. You need an innovative solution that connects strategy to execution and adapts as fast as your priorities change.

This is where Artificial Intelligence (AI) plays a critical role in on-strategy execution. From predicting risks before they derail projects to recommending resource shifts in real time, AI turns your portfolio management approach into a value delivery engine built for speed and proactive decision making that delivers measurable impact.

What Strategy Execution Laggards Still Get Wrong

Strategy Execution Leaders are embracing AI-powered portfolio management solutions to stay ahead of the curve. Laggards, on the other hand, are stuck in old habits like:

• Relying on gut instincts rather than data to make decisions
• Using disconnected tools instead of an integrated portfolio management solution
• Looking backwards with reactive reporting rather than to the future with predictive insights

These habits come with significant consequences, including:

• Slower decision making
• Blind spots that lead to misalignment and missed opportunities
• Time, money, and resources wasted on low-impact work

The numbers don’t lie. Only 6% of Laggards use AI across the organization and Laggards only achieve 49% of their strategic goals.

Compared to Strategy Leaders, who are 8x more likely to use AI and achieve 72% of their strategic goals.

Learn what sets Strategy Execution Leaders apart from the middle-of-the-pack and laggards. See how your organization compares. Take the practical steps to ensure your strategy stays connected to execution. Download the 2025 Benchmark Study: The State of Strategy Execution: Balancing Speed, Adaptation, and Control

Yes, AI is the New Standard… but There’s More to It Than That

There’s a lot of talk about AI right now and how it can fix everything.

But here’s the truth: AI without intent is just another shiny tool.

What organizations really need is AI that serves a purpose. In the case of strategy execution, the purpose is to enhance the core functions of portfolio management, enabling you to perform faster, better, and smarter. Here’s how:

Planning and prioritization

Many organizations struggle to align investments with business objectives because disconnected tools and outdated reports hide whether the strategy is actually being executed. A portfolio management solution closes this gap by consolidating data, providing real-time visibility, and surfacing misalignment as it happens. AI takes this further with predictive insights, what-if modeling, and continuous monitoring, helping Leaders anticipate underperformance, evaluate resource shifts before committing, and maintain balanced portfolios.

The result: strategy and delivery stay synchronized, resources flow to what matters most, and the hidden cost of misalignment disappears.

Delivery and resource management

Project teams face competing priorities, resource constraints, and risks that often stay hidden until they cause delays. Bottlenecks, such as overallocated skills, slipping dependencies, or scope creep, are usually discovered through escalation – when options are limited and replanning is costly.

An effective portfolio management solution mitigates these challenges by providing visibility into resource allocation, making it easier to identify and address those risks before they become problems. With AI, you can perform those functions even more easily and with greater accuracy. Predictive analytics leverages current and historical data to flag bottlenecks and overruns. Leaders can use AI to see what work is at risk, where skills are overallocated, and which dependencies are on track or threatened.

This absolutely transforms resource management and work delivery by moving from reactive, fire-drill scenarios to seamlessly balancing competing demands.

The result: teams deliver high-value work on time with fewer surprises and setbacks.

Closing the Strategy-Execution Gap with AI and Portfolio Management

In a market that rewards speed and adaptability, being a Laggard is more than just inefficient – it’s being a liability. The organizations that are leading the pack don’t rely on spreadsheets, disparate tools, and gut instinct to inform their decision making. They’re using portfolio management solutions powered by AI to:

• Make informed decisions in minutes, not weeks
• Anticipate risks before they escalate into problems
• Identify and invest in initiatives that align with strategy goals

The gulf between Leaders and Laggards is only going to widen over time.

Strategy Leaders are 13x more likely to say it’s “very easy” to gather and analyze decision-making data. Meanwhile, Laggards struggle with blind spots and missed opportunities.

Discover how Leaders use AI and portfolio management at the strategic and project level.

Download our eBook,“Go from Missed Goals to Measurable Impact with AI,” to learn more. We’ll show you how to use AI to ensure your time, money, and resources are spent on the work that makes the most sense.

Consider the latest data from McKinsey. Their November State of AI Report states that 88% of organizations now report regular AI use in at least one business function. That’s up from 78% a year ago, signaling a significant chunk of organizations with their toe in the AI waters.

With such high experimentation levels, business and technology leaders are naturally seeking to scale the technology – and the ROI – just as deeply. What does this signal for the future of AI, and how does software support such an evolving digital landscape?

Planview CEO Razat Gaurav sat down with Nasdaq’s Kristina Ayanian to explore those questions; plus, they discussed how AI is reshaping enterprise decision-making and the pivotal launch of Planview Anvi^TM.

Whether you’re a business leader planning digital transformation or exploring AI solutions for the workplace, this conversation offers valuable insights and predictions about the impact of enterprise AI on productivity, team structure, and organizational success.

Note: The transcript below has been edited for flow and clarity.

What is Connected Work and How Has Planview Evolved?

Razat: Planview started as a quintessential All-American startup in Austin, Texas. Our whole genesis was to bring together resources, projects, and financials into a single frame. We evolved from there into a portfolio planning solution provider and got into lean-agile ways of working.

As our customers evolved, we found that organizations were looking to manage all sorts of work. That’s when we connected the dots between how projects, products, resources, financials, and outcomes all relate to each other.

Combining that with all these new data technologies – including graph technologies, semantic layers, and more – we created what we call “connected work” in the enterprise. That foundation, layered in with all the advancements in AI, is what brings us here today.

Kristina: You’re really that connecting bridge between all the verticals of an enterprise.

Razat: That’s exactly right. It’s connecting their strategic priorities to the actual execution of work and outcomes. It’s also connecting ideation, planning, delivery, and ultimately the outcomes that happen, as well as the handoffs.

It’s connecting the dots between all the different ways of working, because organizations and projects have different approaches – some are more waterfall, some are more agile, some are more hybrid. We support all of those different combinations through our platform.

Enterprise AI in Action: 50+ Million Projects and Real Results

Razat: As we grew and scaled our business, we ended up managing an increasing number of projects and product initiatives in our platform.

Today, we have over $400 billion in transformations that are planned and delivered through Planview – over 50 million projects over the last couple of years.

Customers across financial services, manufacturing, insurance, telecom, retail, and logistics – they all are managing projects and products through our platform, so we’re sitting on a massive data set.

And we were on this journey of adding intelligence, predictions, optimizations, and simulations with what’s now called “classic AI.” We already had those capabilities in our platform.

With the advent of generative AI, which was less than three years ago with the ChatGPT demo, we jumped in full throttle and we architected the capability that allows our customers to reimagine how work is done in the enterprise – and it allows them to think about how to bring automations into new sorts of decision-making in a very simplistic, conversational interface.

Digital Team Members: How Anvi Functions as Part of Your Enterprise Team

Kristina: It sounds like Anvi is a team member for the enterprise – it’s a member of their team. How do you ensure this new platform acts as a complement rather than a replacement to existing investments?

Razat: We think of Planview Anvi as a digital persona — to your point, a team member. And the team member today can work as an assistant to human personas. It can also automate work as a team member and allow humans to do other things in the organization while it just does work for you. It automates tasks and a chain of events for you.

Over time, as we develop more capabilities in Anvi, it will also take on the form of a digital persona by itself, where the digital persona is not just executing on workflows or automating them, or just responding to your prompts. It will actually make human-type judgment calls and understand what inputs to look for, what decision framing to have, and then ultimately what outputs to have.

We’re in our labs developing those capabilities – just like the rest of the world – but soon, they will allow us to think of resources and teams in an unconstrained way, leveraging data and leveraging the modern forms of AI.

Read Next: Why Data Architecture is the Hidden Key to Agentic AI

How AI is Supporting Business and Technology Leaders

Kristina: You brought up a really interesting point. How do you see Anvi changing the role of leaders?

Razat: As leaders are grappling with what this could mean for the future, what we’re finding is that you can do a whole lot more work and create a whole lot more velocity.

We have this notion of “flow” in how work gets done – whether it’s project work or digital work – and the flow velocity can grow tremendously, leveraging AI like Anvi. We’re also seeing productivity grow significantly. We have the instrumentation, the metrics, and the framing of how we’re measuring that with our customers.

Everyone wants the answer today – but it’s going to take some time to evolve – AI can have a lot more profound implications around what roles exist in organizations, between human roles vs. digital roles.

How are organizations structured? Do the functional structures of organizations make sense today? Or do they need to change?

There are a lot of “known unknowns” to navigate, and we want to do all of that in a way that is still safe and has the right level of governance, security, and privacy elements. Those are the things we’re innovating on and working through very closely with our global base of 3,000 customers.

The Future of Enterprise Work: What to Expect by 2035

Kristina: Speaking of the future, where do you see connected work really going in the next five to 10 years? And how does Planview fit into that?

Razat: The immediate opportunity is to increase velocity and increase productivity for existing forms of work. Over time, though, we’re going to see the next wave of evolution where these digital personas – these agents, like those within Anvi – they encompass either an agent as a team member, like a digital persona, or playing an orchestration role across multiple digital agents and human resources.

Over time, I see AI being able to take on a lot more work in the enterprise. That doesn’t mean that humans become irrelevant – I think there’ll be a role for humans, but the role will be different than what it is today.

Also, the scaling of organizations will look very different. You could have organizations that are a lot smaller today, but have an outsized impact because of the digital agentic framework and the access to the right datasets.

Read Next: Mapping the Future of AI Agents in the Enterprise

Now, that all doesn’t impact every industry. AI will impact different industries differently. If you’re a bank, an insurance company, or more of a services-oriented business that is less tied to a physical asset, AI is going to have a bigger impact. If you’re tied to a physical infrastructure with operations and assets, it will have a different approach. But the work in the enterprise is going to be reimagined in a dramatic way.

Ultimately, it’s going to lead to positive things. Are there a lot of things to watch out for, and the right safeguards to have, and the right guardrails to have? Absolutely. But over time, I’m an optimist.

We as a human society will figure out the right guardrails to keep AI for creating value for humans, as opposed to creating value for some other unknown digital entity. This is going to be good for human individuals, organizations, and teams, but also for society, in general.

Adaptability: The Key to Enterprise AI Success

Kristina: It’s all about adaptability.

Razat: You’ve got to adapt – Darwin taught us that. That’s easier said than done, because, as humans, we resist change. I don’t like to change every day. But we’re going to have to – it’s going to really tax our muscles around change, agility, and adaptability.

Ultimately, what will help us in that journey of change and adaptability is why we’re doing it – what is the value in it? And if there is incremental, marginal unit value in that change, then we will do the right things as a human species.

Kristina: Well, I for sure am excited for our future conversation so we can reflect on this one and see how far we’ve come.

Explore AI-Powered Connected Work

As Razat and Kristina’s conversation shows, we’re still in the early stages of understanding AI’s full impact on enterprise operations. The shift toward digital team members and connected work platforms is happening now, but each organization’s journey will be unique.

If you’re curious about how these concepts apply to your enterprise, watch the Planview Anvi demo on demand. The demo offers a practical look at how conversational AI can help teams navigate complex enterprise data and streamline decision-making processes.

Over the past four years, the business environment has continued to evolve at an accelerated pace. To understand how organizations have adapted, Planview commissioned a follow-up study in 2025. The results reveal a striking paradox: while organizations now execute faster than ever, confidence is declining. Only 28% say they can adapt quickly to change, down from 40% in 2021. The reactive responses that worked during acute disruptions are insufficient for navigating constant change.

Planview’s 2025 State of Strategy Execution Benchmark Report identifies what distinguishes today’s high performers. This blog highlights key insights and actionable strategies for effective execution in 2025.

The Balance Imperative: Speed, Adaptability, and Governance

Strategy Execution Leaders exceeded their revenue goals by 12.1%, while Laggards fell short.

Leaders have mastered systematic adaptability: the ability to adjust strategies proactively through established processes rather than crisis management.

This creates a sustainable competitive advantage that maintains speed without sacrificing quality or oversight.

Five Recommendations to Level Up Strategy Execution

Based on an analysis of Leaders’ best practices, Planview recommends the following five steps to build a fast, balanced, and coordinated strategy execution engine.

1. Build Internal Agility to Adapt to Change

Many organizations remain internally focused, constantly firefighting operational issues rather than anticipating and responding to market shifts. With organizations reporting increased risks across multiple areas—including lost opportunities, decline in growth, and major profit loss—these internal barriers create a real competitive vulnerability.

Leading organizations rate internal processes as less of a barrier, demonstrating higher adaptability and faster decision-making. They’ve restructured their approach to prioritize building systematic capabilities for external responsiveness while resolving the friction points that slow internal decision-making

2. Establish Regular Review and Adjustment Cycles

Traditional annual planning cycles leave organizations operating with outdated assumptions for months. By the time reviews happen, market conditions have often shifted dramatically, creating execution drift where teams work hard on initiatives that no longer align with reality.

Top performers treat strategy as a dynamic GPS rather than a static roadmap, conducting reviews at least quarterly, often monthly, for targeted adjustments that keep execution aligned with evolving conditions.

3. Evolve Success Metrics Beyond Operational Efficiency

Most organizations measure success through operational lenses—budget adherence, timelines, and cost efficiency. While these matter, they don’t capture a complete picture of strategic health or long-term competitive position.

Leading organizations have expanded their definition of success to include people-centric indicators—like customer and employee satisfaction—that better predict sustainable advantage and long-term value.

4. Strengthen Alignment with Standardized Goal Frameworks

Alignment problems can stem from inconsistent goal-setting approaches across teams that fail to focus the organization on shared objectives and values. When departments lack clear goal-setting frameworks or employ inconsistent approaches, strategic priorities can get lost in translation, resulting in fragmented execution and wasted resources.

Top performers establish a shared language and consistent prioritization processes that enable faster and more confident decision-making across all organizational levels.

5. Invest in Integrated Technology Platforms and Centralized Coordination

Data silos and disconnected tools create blind spots that slow strategic response. Teams make decisions on incomplete information, leading to misaligned priorities and missed opportunities.. The most successful organizations have solved information flow and decision coordination, creating structures that enable rapid, informed responses while maintaining strategic coherence

On unsteady ground, balance wins over raw speed

Planview’s 2025 global benchmark report shows a shift in how organizations approach strategy execution. Amid constant changes in customer behavior, technology, and regulations, speed alone is no longer the differentiator.

Top performers adjust with purpose, make informed decisions quickly, and maintain enough structure to keep teams aligned. Optimizing this balance between speed, adaptability, and governance is key to executing a competitive strategy effectively.

For many technology executives, it’s like standing in a kitchen mid–breakfast rush—heat rising, pans everywhere, eggs already cracking. You’re managing a dozen tabs, a dozen tools, and a dozen conflicting stories about what’s really happening. You’ve got intuition, but not alignment. What you need isn’t more data—it’s a clean, reliable workspace and a single version of the truth everyone can rally around.

In the recent webinar, “A Day in the Life: How an EVP, Product Development Leverages Value Stream Metrics for Strategic Decision Making,” Beth Weeks (EVP, Product Development at Planview) introduces a practical framework designed specifically for this shift—from scattered, reactive explanations to a disciplined, visibility-driven workflow. Instead of jumping between disconnected numbers, she demonstrates how to utilize a consistent set of value stream metrics to understand where work slows down, how capacity is being utilized, and what influences time-to-market.

It’s a modern executive operating approach—one that replaces the morning scramble with a well-run kitchen, helping leaders answer delivery questions with real data and turn visibility into faster, smarter, and more aligned outcomes.

The Scramble: Why Leaders Still Struggle to Answer Basic Delivery Questions

Beth calls out a hard truth: Executives don’t struggle because they lack capability—they struggle because their data landscape is fragmented beyond recognition.

Teams use different tools and solutions. Those tools and solutions use different workflows. Naming conventions, taxonomies, and processes vary wildly. Even the definition of “Feature” shifts from team to team.

The result is a messy, unpredictable environment where leaders:

• Can’t reconcile metrics across systems
• Spend hours (or days) assembling status reports
• Get blindsided by late-stage surprises
• Lack visibility into bottlenecks and dependencies
• Struggle to connect capacity, demand, and strategic priorities

Beth calls this the swivel-chair executive experience—jumping from tool to tool, board to board, tab to tab, attempting to reconcile mismatched data into something leaders can act on. And when that’s the norm, the problem isn’t leadership. It’s visibility.

Creating an Operating Model Grounded in Flow Metrics

Beth’s approach isn’t to add more data. Instead, it’s about normalizing data leaders already have.

Rather than adding more dashboards or reports, she focuses her approach on building a single, shared language for understanding work progress. Teams can keep using the tools they already rely on—Jira, ADO, ServiceNow, AgilePlace—while knowing that every data point rolls up into one unified, connected model.

By standardizing definitions, aligning taxonomies, and grounding every conversation in the same set of flow metrics, Beth transforms a scattered data landscape into a coherent system executives can trust. The power doesn’t come from adding more information—it comes from making existing information comparable, connected, and actionable.

Once that foundation is in place, the real value emerges: leaders can finally see across silos. Instead of debating which numbers are right, they discuss what those numbers mean—and what to do next. Flow metrics—such as Flow Time, Flow Load, Flow Efficiency, Flow Velocity, and Flow Distribution—become the common language for understanding performance across products and teams. They reveal where work moves smoothly, where it waits, and how capacity aligns with demand. Delivery reviews shift from reactive explanations to proactive conversations about flow, predictability, and outcomes. With a shared language and consistent data model, alignment stops being a manual effort and becomes part of how the organization operates every day.

The Executive Framework for a Modern Operating System

When Beth runs her product development organization, she doesn’t rely on more dashboards or reports—she relies on a system. Her operating framework, powered by Planview Viz, turns fragmented data into clear, actionable insight that drives predictable delivery.

Instead of reacting to status updates, Beth uses Viz to normalize data across tools, uncover systemic bottlenecks, and guide conversations around facts, not assumptions. The six-step framework is simple but transformative—it’s how she replaces the scramble with a system built on visibility, accountability, and evidence.

1. Normalize and Align: Build a Single Language for Work

Beth starts by eliminating chaos at the source. Before Viz, every team defined “Feature,” “In Progress,” and “Done” differently, creating a tangle of taxonomies across Jira, ADO, ServiceNow, and AgilePlace.

With Planview Viz, she normalizes these definitions into a shared model:

• A Feature in one team means the same thing across all teams
• Workflow stages follow a consistent value stream
• Delivery trends are comparable without manual translation

The result: a single, defensible view of work that executives can trust—no matter where it originates. This foundation turns data into insight and alignment into action.

2. Measure Flow Time: Understand True Delivery Speed

Predictability starts with Flow Time, the metric that shows how long work truly takes from start to finish. Viz replaces anecdotal status updates with evidence, revealing:

• End-to-end delivery duration
• Realistic baselines for forecasting
• Trends that show where improvement happens—or stalls

It’s the metric that turns uncertainty into foresight and makes executive forecasting reliable.

3. Find the Real Constraint: Where Work Waits

Most delays aren’t development problems—they’re waiting problems. Beth uses Viz to surface where work is stuck, not just where it’s slow.
Viz shows:

• How long work sits before coding
• Where reviews and approvals queue up
• Which dependencies create recurring delays

Like a well-run kitchen, the goal isn’t to make chefs move faster—it’s to keep the flow of dishes continuous and balanced.

4. Calibrate Flow Load: Balance Demand and Capacity

Leadership isn’t about asking teams to go faster—it’s about knowing when they’re already overloaded. Viz exposes when teams operate above healthy limits by visualizing Flow Load:

• When demand exceeds capacity
• How overload impacts cycle time and predictability
• Where to reset expectations or reprioritize work

This single view changes executive conversations from blame to balance.

5. Diagnose with Flow Efficiency: Find Root Causes Instantly

With Flow Efficiency, Beth moves from data collection to insight generation. Viz shows the ratio of time spent actively working versus waiting, revealing:

• Where bottlenecks appear across the value stream
• Which stages drag down overall performance
• Where leadership intervention will produce the highest ROI

Root causes that once took weeks to uncover now surface in minutes—no escalations required.

6. Lead with Clarity: Adopt an Executive View That Connects Strategy to Delivery

Beth’s framework culminates in a single executive view powered by Viz—one that unites teams, tools, and outcomes.
Executives gain:

• Standardized, comparable metrics across the organization
• Trend lines tied to strategic objectives
• Risk alerts before they escalate
• A clear narrative that connects delivery performance to business value

Instead of requesting status, leaders analyze it. Instead of reacting to surprises, they anticipate them. Visibility becomes operational, not observational.

Why This Operating Framework Matters—and How It Redefines Executive Leadership

Beth’s framework isn’t just a new way to view data—it’s a new way to lead. When executives ground decision-making in flow metrics, conversations shift from debating reports to acting on shared insights. Teams align around one version of reality, risks surface early, and predictability becomes achievable.

With visibility into flow metrics and system-level bottlenecks, leaders can finally manage the system—not chase the symptoms. Visibility becomes the foundation for strategic decisions, stronger collaboration, and faster, more reliable delivery.

It’s the shift from reacting to anticipating, from firefighting to proactive guidance, from scattered data to a single, trusted operating model. Beth’s approach replaces swivel-chair leadership with clarity, confidence, and control—showing what modern executive visibility truly looks like.

To see this framework in action—and how it transforms executive decision-making—watch the on-demand webinar and learn how to bring systemized visibility to your own organization.